Urinary retention is a disease of the urinary tract that prevents hundreds of thousands of people from properly emptying the contents of their bladder. Urinary retention can be caused by many things such as natural aging, acute trauma (especially during surgical procedures), diabetes, multiple sclerosis and others. Despite the range of causes, a feature common to most cases of urinary retention is a reduced neural sensitivity for detecting fluid flow in the urethra. This reduction in sensitivity, in turn, limits the effectivenes of reflexes that naturally control bladder voiding. We hypothesize that enhancing urethral sensitivity will mitigate many of the symptoms associated with retention by allowing the reflexes to once again function properly. The work in this proposal will demonstrate that it is possible to enhance urethral sensitivity. To accomplish this enhancement we will use a process called stochastic resonance, where we inject sub-threshold levels of electrical noise (amounts small enough that they cannot be detected by the sensory neurons) directly into the urethra. This will be done in a rat model of the urinary tract. Stochastic resonance has been successful in other biological sensory systems and we hypothesize that these undetectable perturbations will lower the activation threshold of the urethral sensory neurons, effectively increasing their sensitivity o flow. We will demonstrate our findings by recording from the pudendal nerve, which carries sensory information from the urethra to the spinal cord. We will compare the pudendal nerve's response to fluid flow in the urethra both with and without the stimulation at a range of flowrates We can conclude that sensitivity was enhanced if more neural activity is observed in the presence of the stimulation than without it, and if slower flowrates can be detected with the stimulation it will also serve as evidence of enhanced sensitivity. Finally, a mathematical model of stochastic resonance in the lower urinary tract will be developed in tandem with the animal experiments. This model will allow us to simulate many different types of stimulation and flowrates to better guide the animal work. Using the model we can investigate a wide range of experiment parameters, many more than we could test using the biological system. We will use the model estimates of the optimal stimulation parameters and flowrates to give ourselves the best chance of success for enhancing urethral sensitivity.